_base_ = "../rpn/rpn_r50_caffe_fpn_1x_coco.py"
model = dict(
    rpn_head=dict(
        _delete_=True,
        type="CascadeRPNHead",
        num_stages=2,
        stages=[
            dict(
                type="StageCascadeRPNHead",
                in_channels=256,
                feat_channels=256,
                anchor_generator=dict(
                    type="AnchorGenerator",
                    scales=[8],
                    ratios=[1.0],
                    strides=[4, 8, 16, 32, 64],
                ),
                adapt_cfg=dict(type="dilation", dilation=3),
                bridged_feature=True,
                sampling=False,
                with_cls=False,
                reg_decoded_bbox=True,
                bbox_coder=dict(
                    type="DeltaXYWHBBoxCoder",
                    target_means=(0.0, 0.0, 0.0, 0.0),
                    target_stds=(0.1, 0.1, 0.5, 0.5),
                ),
                loss_bbox=dict(type="IoULoss", linear=True, loss_weight=10.0),
            ),
            dict(
                type="StageCascadeRPNHead",
                in_channels=256,
                feat_channels=256,
                adapt_cfg=dict(type="offset"),
                bridged_feature=False,
                sampling=True,
                with_cls=True,
                reg_decoded_bbox=True,
                bbox_coder=dict(
                    type="DeltaXYWHBBoxCoder",
                    target_means=(0.0, 0.0, 0.0, 0.0),
                    target_stds=(0.05, 0.05, 0.1, 0.1),
                ),
                loss_cls=dict(
                    type="CrossEntropyLoss", use_sigmoid=True, loss_weight=1.0
                ),
                loss_bbox=dict(type="IoULoss", linear=True, loss_weight=10.0),
            ),
        ],
    ),
    train_cfg=dict(
        rpn=[
            dict(
                assigner=dict(
                    type="RegionAssigner", center_ratio=0.2, ignore_ratio=0.5
                ),
                allowed_border=-1,
                pos_weight=-1,
                debug=False,
            ),
            dict(
                assigner=dict(
                    type="MaxIoUAssigner",
                    pos_iou_thr=0.7,
                    neg_iou_thr=0.7,
                    min_pos_iou=0.3,
                    ignore_iof_thr=-1,
                    iou_calculator=dict(type="BboxOverlaps2D"),
                ),
                sampler=dict(
                    type="RandomSampler",
                    num=256,
                    pos_fraction=0.5,
                    neg_pos_ub=-1,
                    add_gt_as_proposals=False,
                ),
                allowed_border=-1,
                pos_weight=-1,
                debug=False,
            ),
        ]
    ),
    test_cfg=dict(
        rpn=dict(
            nms_pre=2000,
            max_per_img=2000,
            nms=dict(type="nms", iou_threshold=0.8),
            min_bbox_size=0,
        )
    ),
)
optimizer_config = dict(_delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
